Detection of rice sheath blight for in-season disease management using multispectral remote sensing

文献类型： 外文期刊

第一作者： Qin, ZH

作者： Qin, ZH;Zhang, MH

作者机构：

关键词： rice disease;remote sensing;sheath blight;agriculture;crop;diagnosis

期刊名称：INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION （ 影响因子：5.933； 五年影响因子：6.225 ）

ISSN： 0303-2434

年卷期： 2005 年 7 卷 2 期

页码：

收录情况： SCI

摘要： Timely diagnosis of crop diseases in fields is critical for precision on-farm disease management. Remote sensing technology can be used as an effective and inexpensive method to identify diseased plants in a field scale. However, due to the diversity of crops and their associated diseases, application of the technology to agriculture is still in research stage, which needs to be elaborately investigated for algorithm development and standard image processing procedures. In this paper, we examined the applicability of broadband high spatial-resolution ADAR (Airborne Data Acquisition and Registration) remote sensing data to detect rice sheath blight and developed an approach to further explore the applicability. Based on the field symptom measurements, a comprehensive field disease index (DI) was constructed to measure infection severity of the disease and to relate to image sampled infections. In addition to direct band digital number (DN) values, band ratio indices and standard difference indices were used to examine possible correlations between field and image data. The results indicated that the broadband remote sensing imagery has the capability to detect the disease. Some image indices such as RI14, SDI14 and SDI24 worked better than others. A correlation coefficient above 0.62 indicated that these indices would be valuable to use for identification of the rice disease. In the validation analysis, we obtained a small root mean square error (RMS = 9.1), confirming the applicability of the developed method. Although the results were encouraging, it was difficult to discriminate healthy plants from light infection ones when DI < 20 because of their spectral similarities. Hence, it was clear that identification accuracy increases when infection reaches medium-to-severe levels (DI > 35). This phenomenon illustrated that remote sensing images with higher spectral resolution (more bands and narrower bandwidth) were required in order to further examine the capability of separating the light diseased plants from healthy plants. (c) 2005 Elsevier B.V. All rights reserved.

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